Damage Pattern Recognition and Crack Propagation Prediction for Crumb Rubber Concrete Based on Acoustic Emission Techniques

Jianjie Sun,Giuseppe Lacidogna,Zhengwu Fu,Xi Chen

doi:10.3390/app112311476

Jianjie Sun, Giuseppe Lacidogna + Show 2 more

Open Access

https://doi.org/10.3390/app112311476

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Abstract

In this study, the clustering method of the concrete matrix rupture and rubber fracture damages as well as the prediction of the ultimate load of crumb rubber concrete using the acoustic emission (AE) technique were investigated. The loading environment of the specimens was a four-point bending load. Six clustering methods including k-means, fuzzy c-means (FCM), self-organizing mapping (SOM), Gaussian mixture model (GMM), hierarchical model, and density peak clustering method were analyzed; the results illustrated that the density peak clustering has the best performance. Next, the optimal clustering algorithm was used to cluster AE signals so as to study the evolution behavior of different damage modes, and the ultimate load of crumb rubber concrete was predicted by an artificial neural network. The results indicated that the combination of AE techniques and appropriate clustering methods such as the density peak clustering method and the artificial neural network could be used as a practical tool for structural health monitoring of crumb rubber concrete.

Highlights

In recent years, the production of automobiles has led to the accumulation of numerous waste tires that are difficult to dispose of, resulting in a large amount of space occupied and a high fire hazard
In order to understand the development of microcracks and the monitoring of fracture-causing behaviors, Xu et al [26] have conducted experimental studies based on acoustic emission (AE) technology
The research of this paper consists of three main parts: firstly, six clustering methods for analyzing AE signals are introduced in Section 2, including k-means, fuzzy c-means (FCM), Gaussian mixture model (GMM), selforganizing mapping (SOM), hierarchical model, and density peak clustering; secondly, the optimal clustering method is derived by comparative analysis in Section 3.1 and used to identify damage patterns of crumb rubber concrete; in Section 3.2, the artificial neural network algorithm with input data as acoustic emission parameters is used in this paper to predict the ultimate load of crumb rubber concrete for the first time

Summary

Introduction

The production of automobiles has led to the accumulation of numerous waste tires that are difficult to dispose of, resulting in a large amount of space occupied and a high fire hazard. If the acoustic emission signals of the microcracks at various stages in different modes can be monitored and a reasonable analysis made, it will bring benefits to practical engineering applications For this reason, this paper used AE technology to conduct damage monitoring research on crumb rubber concrete. The research of this paper consists of three main parts: firstly, six clustering methods for analyzing AE signals are introduced, including k-means, FCM, GMM, SOM, hierarchical model, and density peak clustering; secondly, the optimal clustering method (density peak clustering) is derived by comparative analysis in Section 3.1 and used to identify damage patterns of crumb rubber concrete; in Section 3.2, the artificial neural network algorithm with input data as acoustic emission parameters is used in this paper to predict the ultimate load of crumb rubber concrete for the first time The research of this paper consists of three main parts: firstly, six clustering methods for analyzing AE signals are introduced in Section 2, including k-means, FCM, GMM, SOM, hierarchical model, and density peak clustering; secondly, the optimal clustering method (density peak clustering) is derived by comparative analysis in Section 3.1 and used to identify damage patterns of crumb rubber concrete; in Section 3.2, the artificial neural network algorithm with input data as acoustic emission parameters is used in this paper to predict the ultimate load of crumb rubber concrete for the first time

Materials and Apparatus

Introduction to Density Peak Clustering Method

Clustering Results of Common Clustering Methods